Improving the nutritional evaluation in head neck cancer patients using bioelectrical impedance analysis: Not only the phase angle matters
Metadatos
Mostrar el registro completo del ítemEditorial
Wiley
Fecha
2024-08-06Referencia bibliográfica
A.D. Herrera-Martínez et al. Bioelectrical impedance analysis in head neck cancer. Journal of Cachexia, Sarcopenia and Muscle (2024). DOI: 10.1002/jcsm.13577
Patrocinador
FRESENEIUS KABI®; Instituto de Salud Carlos III (ISCIII) PI23/01554; European Union, JR19/00050, ISCIIIResumen
Background Malnutrition and sarcopenia are highly prevalent in patients with head neck cancer (HNC). An accurate
early diagnosis is necessary for starting nutritional support, as both are clearly associated with clinical outcomes and
mortality. We aimed to evaluate the applicability and accuracy of body composition analysis using electrical
bioimpedance vectorial analysis (BIVA) for diagnosing malnutrition and sarcopenia in patients with HNC cancer undergoing
systemic treatment with chemotherapy or radiotherapy.
Methods Cross-sectional, observational study that included 509 HNC patients. A comprehensive nutritional evaluation
that included BIVA was performed.
Results The prevalence of malnutrition was higher in patients that received treatment with chemotherapy (59.2% vs.
40.8%, P < 0.001); increased mortality was observed in malnourished patients (33.3% vs. 20.1%; P < 0.001); ECOG
status (1–4) was also worse in malnourished patients (59.2% vs. 22.8% P < 0.001). Body cell mass (BCM) and fat mass
were the most significantly associated parameters with malnutrition [OR 0.88 (0.84–0.93) and 0.98 (0.95–1.01), respectively];
BCM and fat free mass index (FFMI) were associated with several aspects including (1) the
patient-generated subjective global assessment [OR 0.93 (0.84–0.98) and 0.86 (0.76–0.97), respectively], (2) the presence
of sarcopenia [OR 0.81 (0.76–0.87) and 0.78 (0.66–0.92), respectively]. A BCM index (BCMI) < 7.8 in combination
with other parameters including FFMI and BCM accurately predicted patients with malnutrition [accuracy 95% CI:
0.803 (0.763–0.839); kappa index: 0.486; AUC: 0.618 (P < 0.01)]. A BCMI cutoff of 7.6 was enough for identifying
males with malnutrition (P < 0.001), while it should be combined with other parameters in females.
Conclusions Body composition parameters determined by BIVA accurately identify patients with HNC and malnutrition.
Phase angle, but other parameters including BCMI, FFMI and BCM provide significant information about nutritional
status in patients with HNC.